195 research outputs found
Answer Set Programming Modulo `Space-Time'
We present ASP Modulo `Space-Time', a declarative representational and
computational framework to perform commonsense reasoning about regions with
both spatial and temporal components. Supported are capabilities for mixed
qualitative-quantitative reasoning, consistency checking, and inferring
compositions of space-time relations; these capabilities combine and synergise
for applications in a range of AI application areas where the processing and
interpretation of spatio-temporal data is crucial. The framework and resulting
system is the only general KR-based method for declaratively reasoning about
the dynamics of `space-time' regions as first-class objects. We present an
empirical evaluation (with scalability and robustness results), and include
diverse application examples involving interpretation and control tasks
Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs
We establish a novel relation between delete-free planning, an important task
for the AI Planning community also known as relaxed planning, and logic
programming. We show that given a planning problem, all subsets of actions that
could be ordered to produce relaxed plans for the problem can be bijectively
captured with stable models of a logic program describing the corresponding
relaxed planning problem. We also consider the supported model semantics of
logic programs, and introduce one causal and one diagnostic encoding of the
relaxed planning problem as logic programs, both capturing relaxed plans with
their supported models. Our experimental results show that these new encodings
can provide major performance gain when computing optimal relaxed plans, with
our diagnostic encoding outperforming state-of-the-art approaches to relaxed
planning regardless of the given time limit when measured on a wide collection
of STRIPS planning benchmarks.Comment: Paper presented at the 39th International Conference on Logic
Programming (ICLP 2023), 14 page
Theory Solving Made Easy with Clingo 5
Answer Set Programming (ASP) is a model, ground, and solve paradigm. The integration of application- or theory-specific reasoning into ASP systems thus impacts on many if not all elements of its workflow, viz. input language, grounding, intermediate language, solving, and output format. We address this challenge with the fifth generation of the ASP system clingo and its grounding and solving components by equipping them with well-defined generic interfaces facilitating the manifold integration efforts. On the grounder\u27s side, we introduce a generic way of specifying language extensions and propose an intermediate format accommodating their ground representation. At the solver end, this is accompanied by high-level interfaces easing the integration of theory propagators dealing with these extensions
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